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\setcounter{figure}{0}
\pdfbookmark[1]{Appendix Figures}{appx-figures}

```{=latex}
\threepanelfig
%%%%% Full Figure Config %%%%%
    {breaks_adjustment} % label
    {Correction of Structural Breaks in Spending, Small Business Revenue, and Employment} % title
%%%%% Panel A %%%%%
    {../results/Spending/Structural Break Affinity} % filepath
    {breaks_affinity} % label
    {Break Adjustment - Spending (Ulster County, NY)} % title
%%%%% Panel B %%%%%
    {../results/Small Business Revenue/Small Businesses Open sharp drop Connecticut} % filepath
    {breaks_womply} % label
    {Break Adjustment - Small Business Revenue (Connecticut)} % title
%%%%% Panel C %%%%%
    {../results/Employment/Before and After Quartile Threshold Adjustment - Q2 and Q3} % filepath
    {breaks_emp} % label
    {Break Adjustment - Employment (National, Q2 and Q3)} % title
%%%%% Notes %%%%%
    {This figure presents examples of the data adjustment procedures described in Section \ref{sec:Data-Series} for the consumer spending series (Panel A), the small business revenue series (Panel B) and the combined Paychex-Intuit employment series (Panel C). Data sources: Affinity Solutions, Womply, Paychex, Intuit.}
```

```{=latex}
\twopanelnarrowfig
%%%%% Full Figure Config %%%%%
    {ind_shares} % label
    {Industry Shares of Consumer Spending and Business Revenues Across Datasets} % title
%%%%% Panel A %%%%%
    {../results/Spending/QSS, Affinity, Womply Industry Spending Shares} % filepath
    {ind_shares_qss} % label
    {Compared to QSS} % title
%%%%% Panel B %%%%%
    {../results/Spending/MRTS, Affinity, Womply Spending Mix} % filepath
    {ind_shares_mrts} % label
    {Compared to MARTS} % title
%%%%% Notes %%%%%
    {This figure compares the industry composition of spending in private sector datasets to the industry composition of spending in representative survey datasets. Panel A shows the NAICS 2-digit industry mix for transactions in the Affinity Solutions and Womply datasets compared with the Quarterly Services Survey (QSS), a survey dataset providing timely estimates of revenue and expenses for selected service industries. Subsetting to the industries in the QSS, each bar represents the share of revenue in the specified sector during Q1 2020. We construct spending and revenue shares for the Affinity Solutions and Womply datasets (respectively) by aggregating card transactions in Q1 2020, using the merchant to classify the purchase by sector. Panel B shows the NAICS 3-digit industry mix for the same two sector private datasets compared with the Advance Monthly Retail Trade Survey (MARTS), another survey dataset which provides current estimates of sales at retail and food services stores across the United States. Subsetting to the industries in the MARTS, each bar represents the share of revenue in the specified sector during January 2020. We construct revenue shares for the private datasets, Affinity and Womply, by aggregating firm revenue from card transactions in January 2020. Data sources: Affinity Solutions, Womply, QSS, MARTS.}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
	{bg_jolts} % label
    {Industry Shares of Job Postings in Lightcast and JOLTS} % title
	{../results/Jobs/Job Openings BG vs JOLTS} % filepath
%%%%% Notes %%%%%
    {This figure presents a scatter plot showing the industry share of each 2-digit NAICS code of job postings in the Job Openings and Labor Turnover Survey (JOLTS) data in January 2020 vs. the corresponding industry share in job postings in Lightcast data in January 2020. The solid line is a 45 degree line. The annotation in the bottom right corner of the panel displays the correlation between 2-digit NAICS industry shares in the JOLTS vs. Lightcast data in January 2020, excluding NAICS 92 (Public Administration), and weighting according to total job openings in each 2-digit NAICS code in JOLTS in January 2020. Data sources: Lightcast, JOLTS.}
```

```{=latex}
\twopanelfig
%%%%% Full Figure Config %%%%%
    {seasonal_fluct} % label
    {Seasonal Fluctuations in Consumer Spending vs. Employment} % title
%%%%% Panel A %%%%%
    {../results/Spending/Seasonal Fluctuations in Consumer Spending in MARTS} % filepath
    {seasonal_spend} % label
    {Seasonal Fluctuations in Consumer Spending in MARTS Data} % title
%%%%% Panel B %%%%%
    {../results/Employment/Seasonal Fluctuations in Employment in CES Data} % filepath
    {seasonal_emp} % label
    {Seasonal Fluctuations in Employment in CES Data} % title
%%%%% Notes %%%%%
    {This figure compares seasonal fluctuations in Advance Monthly Retail Trade Survey (MARTS) data on consumer spending on retail sales and food services (excluding motor vehicle and gas spending) vs. Current Employment Statistics (CES) data on private sector non-farm employment. Panel A shows seasonal fluctuations in consumer spending in MARTS data. The series marked in triangles shows trends in consumer spending without seasonal adjustment, expressed as percentage changes in consumer spending in each month relative to January of the same year. The series marked in circles shows trends in consumer spending, as seasonally adjusted by the U.S. Census Bureau, expressed as percentage changes in consumer spending in each month relative to January of the same year. The annotation in the lower right hand corner displays the RMSE for the difference between the two series. Panel B replicates Panel A using CES data on private sector, non-farm employment. Data sources: MARTS, CES.}
```

```{=latex}
\twopanelwidefig
%%%%% Full Figure Config %%%%%
    {affinity_nipa_marts} % label
    {Consumer Spending in National Accounts vs. Credit and Debit Card Data} % title
%%%%% Panel A %%%%%
    {../results/Spending/Components of Change in GDP} % filepath
    {gdp_changes_q1_q2_2020} % label
    {National Accounts: Changes in GDP and its Components} % title
%%%%% Panel B %%%%%
    {../results/Spending/Benchmark Affinity Against MRTS - Retail & Food} % filepath
    {affinity_vs_marts_month_on_month} % label
    {Affinity Solutions Data vs. Advance Monthly Retail Trade Survey} % title
%%%%% Notes %%%%%
    {This figure examines changes in consumer spending measured in Affinity Solutions credit and debit card data, National Income and Product Accounts (NIPA) data, and Advance Monthly Retail Trade Survey (MARTS) data. Panel A shows the change in GDP from Q1 to Q2 2020 using NIPA data (Tables 1.1.1, 1.1.6 and 2.3.2). The first bar shows the seasonally-adjusted decline in real GDP (\${{gdp_dollars}}T). In parentheses under the first bar we report the compound annual growth rate corresponding to this change in real GDP (-{{gdp_percent}}\%). Bars two through five decompose the change in real GDP, estimated using NIPA Table 1.1.1. The final bar shows the contribution of components of Personal Consumption Expenditures (PCE) that are likely to be captured in credit card spending (\${{cc_dollars}}T), estimated using NIPA Table 2.3.2. This includes all components of PCE except for motor vehicles and parts, housing and utilities, health care, and the final consumption expenditures of nonprofit institutions serving households. This bar indicates total spending (including spending in other modes of payment such as cash) in categories of goods and services which are likely to be well represented in card spending data, rather than total card spending itself. Panel B reports month-on-month changes in average daily spending for each month in the Affinity Solutions credit and debit card data and the Advance Monthly Retail Trade Survey (MARTS). The Food and Accommodation Services series in Panel B restricts to NAICS 72; the Retail series restricts to NAICS 44-45. The MARTS series in Panel B are constructed by dividing the total spending in each category by the number of days in that month, and then indexing to the average daily spending of January 2019. The Affinity series are constructed by taking the monthly average of the seven-day moving average series, indexed to January 2019. We also report the root mean squared error (RMSE) corresponding to the difference between these two series. Data sources: Affinity Solutions, NIPA.}
```

```{=latex}
\twopanelfig
%%%%% Full Figure Config %%%%%
    {affinity_mrts_coinout} % label
    {Consumer Spending Benchmarks, Affinity vs. MARTS and CoinOut} % title
%%%%% Panel A %%%%%
    {../results/Spending/Spending by Industry in Affinity vs MRTS} % filepath
    {affinity_mrts} % label
    {Consumer Spending in Affinity Data vs. MARTS, by Industry \\ \vspace{3mm} April 2020} % title
%%%%% Panel B %%%%%
    {../results/Spending/CoinOut vs Affinity Spending} % filepath
    {affinity_coinout} % label
    {Cash Spending in CoinOut Transactions Data vs. Card Spending} % title
%%%%% Notes %%%%%
    {This figure benchmarks Affinity Solutions data against MARTS and CoinOut data. Panel A displays a scatter plot of changes in spending at the three-digit NAICS code level between January and April 2020 in the Affinity data vs. the MARTS data, restricting to industries where the industry definitions in the Affinity Solutions data align closely with a three-digit NAICS code surveyed in the MARTS. We report the correlation between changes in the Affinity and MARTS data, weighted by total MARTS spending in January 2020. Panel B compares week-to-week changes of national trends in cash transactions in CoinOut data vs. card spending on groceries in Affinity Solutions data in 2020. See Appendix \ref{subsec:Data-Affinity-Masking-and-Publication} for a description of the CoinOut data. Data sources: CoinOut, Affinity Solutions, MARTS.}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
   {covid_isolation_associations_appendix} % label
    {Association Between COVID-19 Incidence and Mobility} % title
    {../results/COVID-19 Incidence/Time outside home vs COVID case rate} % filepath
%%%%% Notes %%%%%
    {This figure presents a county-level binned scatter plot, constructed as described in Figure \ref{fig:covid_isolation_associations}. The y-axis presents the change in time spent away from home from the base period (January 3 to February 6, 2020) to the three-week period of March 25 to April 14, 2020 (see Appendix \ref{sec:Data-Mobility} for details on the time-away-from-home series from Google Community Mobility Reports).  The x-axis variable is the logarithm of the county's cumulative COVID case rate per capita as of April 14, 2020; with axis labels showing the levels on a logarithmic scale. We plot values separately for counties in the top and bottom quartiles of median household income (measured using population-weighted 2014-2018 ACS data). Data sources: Google Community Mobility Reports, New York Times.}
```

```{=latex}
\twopanelfig
%%%%% Full Figure Config %%%%%
    {revenue_spending} % label
    {Small Business Revenue Changes vs. Consumer Spending Changes} % title
%%%%% Panel A %%%%%
    {../results/Spending/Consumer Spending vs Small Business Revenue Changes AFS - new data - changes} % filepath
    {revenue_spending_food} % label
    {Food Services and Accommodation} % title
%%%%% Panel B %%%%%
    {../results/Spending/Consumer Spending vs Small Business Revenue Changes Retail - new data - changes} % filepath
    {revenue_spending_retail} % label
    {Retail Services} % title
%%%%% Notes %%%%%
    {This figure compares month-on-month changes in total consumer spending (from Affinity Solutions data) and small business revenue (from Womply data) between January 2020 and December 2021. The spending series is expressed as a percentage change relative to January 6 to February 2, 2020 and the small business revenue series is expressed as a percentage change relative to January 4 to 31, 2020. We do not seasonally adjust spending or small business revenue in this figure because seasonal fluctuations provide useful variation to assess whether the consumer spending series tracks the small business revenue series. Panel A restricts to food services and accommodation (NAICS code 72), and Panel B restricts to retail trade sectors (NAICS code 44-45). The bottom right corner of each panel reports the root mean squared error (RMSE) corresponding to the difference between the two lines and the correlation between the month-on-month changes series. Data sources: Affinity Solutions, Womply.}
```

```{=latex}
\twopanelfig
%%%%% Full Figure Config %%%%%
    {smallbiz_jobposts_national_trends} % label
    {National Trends in Small Business Revenue and Job Postings for Low-Education Workers} % title
%%%%% Panel A %%%%%
    {../results/Small Business Revenue/Small Business Revenue - National Revenue over time} % filepath
    {smallbiz_national_trend} % label
    {Small Business Revenue, January 2020 to December 2021} % title
%%%%% Panel B %%%%%
    {../results/Jobs/Jobs - National Job Postings for Low-Education Workers over time} % filepath
    {jobposts_national_trend} % label
    {Job Postings for Low-Education Workers, January 2020 to December 2021} % title
%%%%% Notes %%%%%
    {Panel A shows the changes in small business revenue between January 2020 and December 2021, relative to January 4 to 31, 2020. Panel B shows the changes in job postings for low-education workers between January 2020 and December 2021, relative to January 4 to 31, 2020. To address potential spatial mismatch in job postings, in the orange series of panel B, we reweight county-level job postings by the number of bottom-wage-quartile workers in January 2020 (as derived from the Paychex-Intuit data) and aggregate to the national level. Data sources: Womply, Lightcast, Paychex, Intuit.}
```

```{=latex}
\threepanelfig
%%%%% Full Figure Config %%%%%
    {zipmaps_smallbiz} % label
    {Changes in Small Business Revenues by ZIP Code} % title
%%%%% Panel A %%%%%
    {../results/Small Business Revenue/map_smallbiz_byZIP_SF} % filepath
    {zipmaps_smallbiz_SF} % label
    {San Francisco \\ \vspace{3mm} March 23 to April 12, 2020} % title
%%%%% Panel B %%%%%
    {../results/Small Business Revenue/map_smallbiz_byZIP_Chicago} % filepath
    {zipmaps_smallbiz_Chicago} % label
    {Chicago \\ \vspace{3mm} March 23 to April 12, 2020} % title
%%%%% Panel C %%%%%
    {../results/Small Business Revenue/map_smallbiz_byZIP_NYC} % filepath
    {zipmaps_smallbiz_NYC} % label
    {New York City \\ \vspace{3mm} March 23 to April 12, 2020} % title
%%%%% Notes %%%%%
    {This figure plots seasonally-adjusted changes in small business revenue by ZIP code in the MSAs corresponding to San Francisco-Oakland-Hayward, CA (Panel A), Chicago-Naperville-Elgin, IL-IN-WI (Panel B), and New York-Newark-Jersey City, NY-NJ-PA (Panel C). The changes are measured during March 23 to April 12, 2020 relative to January 4 to 31, 2020. We seasonally-adjust revenue in each week by dividing the indexed value relative to January for that week in 2020 by the corresponding indexed value from 2019. These maps must be viewed in color to be interpretable; dark red colors represent areas with larger revenue declines, while dark blue colors represent areas with smaller declines. Data source: Womply.}
```

```{=latex}
\twopanelfig
%%%%% Full Figure Config %%%%%
    {smallbiz_zip_associations_appendix} % label
    {Changes in Small Business Revenues vs. ZIP Code Characteristics} % title
%%%%% Panel A %%%%%
    {../results/Small Business Revenue/Small Business Revenue vs Median Income binscatter} % filepath
    {smallbiz_vs_income} % label
    {Median Income, by ZIP \\ \vspace{3mm} April 2020} % title
%%%%% Panel B %%%%%
    {../results/Small Business Revenue/Small Business Revenue vs Population Density binscatter} % filepath
    {smallbiz_vs_density} % label
    {Population Density, by ZIP \\ \vspace{3mm} April 2020} % title
%%%%% Notes %%%%%
    {This figure presents binned scatter plots showing the relationship between changes in seasonally-adjusted small business revenue in Womply data vs. various local area characteristics at the ZIP code level. The binned scatter plots are constructed as described in Figure \ref{fig:covid_isolation_associations}. In each panel, we measure changes in small business revenue as the average value of our index at the ZIP code level between March 23 and April 12, 2020 (see Section \ref{subsec:Data-Revenue} and Appendix \ref{sec:Data-Womply} for details on the construction of our small business revenue series). In Panel A, the x-axis variable is median household income at the ZIP code level from the 2014-2018 ACS. In Panel B, the x-axis variable is the logarithm of the number of ZIP code inhabitants per square mile in the 2014-18 ACS; with axis labels showing the levels on a logarithmic scale. Data sources: Womply, ACS.}
```

```{=latex}
\ThreePanelFigCustomWidth
%%%%% Full Figure Config %%%%%
    {small_business_binscatters} % label
    {Changes in Small Business Revenues vs. Local Characteristics} % title
%%%%% Panel A %%%%%
    {../results/Small Business Revenue/Small Business Revenue vs Median Rent binscatter - Sector FEs} % filepath
    {revenue_rent_sectorfes} % label
    {Median Two Bedroom Rent, by ZIP; Controlling for Sector FEs} % title
    {0.8\textwidth} % width
%%%%% Panel B %%%%%
    {../results/Small Business Revenue/Changes in Small Business Revenue vs Income Share of Top 1 Percent of Income Distribution} % filepath
    {revenue_vs_top1income} % label
    {Income Share of the Top 1\% of the Local Income Distribution, by County} % title
    {0.45\textwidth} % width
%%%%% Panel C %%%%%
    {../results/Small Business Revenue/Changes in Small Business Revenue vs Share of Population below Poverty Line} % filepath
    {revenue_sharepoverty} % label
    {Share of the Population Below the Poverty Line, by County} % title
    {0.45\textwidth} % width
%%%%% Notes %%%%%
    {This figure shows the association between ZIP-level or county-level characteristics and changes in small business revenues between January 4 to 31, 2020 and March 23 to April 12, 2020, as measured in Womply data. Panel A presents a binned scatter plot of changes in small business revenue at the ZIP x sector (2-digit NAICS) level vs. median two-bedroom rent at the ZIP level, controlling for sector fixed effects. Panels B and C replicate Figure \ref{fig:smallbiz_zip_associations} but compare the declines in small business revenue with various measures of the distribution of income at the county level. Panel B presents a binned scatter plot of changes in small business revenue vs. the income share of the top 1\% of the income distribution within each county, constructed using the distribution of parent incomes in Chetty et al. (2014). The top 1\% of the income distribution is defined using the distribution of incomes within each county, rather than the national income distribution. Panel C presents a binned scatter plot of changes in small business revenue vs. the share of the county population with incomes below the poverty line in the 2014-2018 ACS. The binned scatter plots are constructed as described in Figure \ref{fig:covid_isolation_associations}. Data source: Womply.}
    {}
```

```{=latex}
\twopanelwidefig
%%%%% Full Figure Config %%%%%
    {employment_benchmark} % label
    {Changes in Employment Rates Over Time} % title
%%%%% Panel A %%%%%
    {../results/Employment/Employment in Tracker vs CES vs CPS - changes - long} % filepath
    {employment_benchmark_allindustries} % label
    {Pooling All Industries} % title
%%%%% Panel B %%%%%
    {../results/Employment/Employment AFS and PS in Tracker vs CES - changes - long} % filepath
    {employment_benchmark_byindustry} % label
    {Accommodation and Food Services vs. Professional Services} % title
%%%%% Notes %%%%%
    {This figure compares month-to-month changes in employment, as measured in three different datasets. The series for the combined Paychex-Intuit dataset plots values of the changes measured as of the week of the 15th of a given month relative to the same value on the week of the 15th of the prior month. The series for the Current Employment Statistics (CES) and Current Population Survey (CPS) plot the change to the current monthly value relative to the prior monthly value.  Panel A presents data from these three series pooling employment in all private non-farm sectors.  Panel B repeats Panel A restricting to employment in the Food and Accommodation sector (NAICS 72) and the Professional and Business Services sector (NAICS 61-62). Data sources: Paychex, Intuit, CES, CPS.}
```

```{=latex}
\ThreePanelFigCustomWidth
%%%%% Full Figure Config %%%%%
    {employment_benchmark_ces_qcew} % label
    {Changes in Employment as of July 2020 in Paychex-Intuit Data vs. CES and QCEW} % title
%%%%% Panel A %%%%%
    {../results/Employment/Employment Tracker vs CES by Industry} % filepath
	{emp_ces_by_naics} % label
    {Change in Employment Rates by Industry: Paychex-Intuit vs. CES} % title
    {0.495\textwidth} % width    
%%%%% Panel B %%%%%
    {../results/Employment/Employment Tracker vs CES by State} % filepath
	{emp_ces_by_state} % label
    {Change in Employment Rates by State: Paychex-Intuit vs. CES} % title
    {0.495\textwidth} % width
%%%%% Panel C %%%%%
    {../results/Employment/scatter qcew tracker by cz july 2020} % filepath	
	{emp_qcew_by_county} % label
    {Change in Employment Rates by Commuting Zone: Paychex-Intuit vs. QCEW} % title
    {0.8\textwidth} % width
%%%%% Notes %%%%%
    {This figure benchmarks the Paychex-Intuit combined employment series to the Current Employment Statistics (CES) and the Quarterly Census of Employment and Wages (QCEW). Panel A shows a scatter plot of changes in employment in Paychex-Intuit combined data between January 4 to 31, 2020 and July 17, 2020 vs. changes in CES employment between January and July by industry (2-digit NAICS code), for private non-farm industries. Panel B shows a scatter plot of changes in employment in Paychex-Intuit combined data between January 4 to 31, 2020 and July 17, 2020 vs. changes in CES employment between January and July, by state. Panel C shows a scatter plot of changes in employment in Paychex-Intuit combined data between January 4 to 31, 2020 and July 17, 2020 vs. changes in QCEW employment between January and July, by commuting zone for the 50 largest commuting zones by population. In all panels, the bottom right corner displays the correlation between the data points in each graph, weighted respectively by CES employment in each NAICS code (Panel A), state population (Panel B), and commuting zone population (Panel C). Data sources: Paychex, Intuit, CES, QCEW.}
    {}
```

```{=latex}
\ThreePanelFigCustomWidth
%%%%% Full Figure Config %%%%%
    {employment_benchmark_adp_cps} % label
    {Employment in Paychex-Intuit Data vs. ADP and CPS} % title
%%%%% Panel A %%%%%
    {../results/Employment/Employment Tracker vs ADP by Income Quartile} % filepath
    {emp_adp_by_incq} % label
    {Trends in Employment Rates by Wage Quartile: Paychex-Intuit vs. ADP} % title
    {0.8\textwidth} % width
%%%%% Panel B %%%%%
    {../results/Employment/Employment Tracker vs ADP vs CPS by Income Quartile} % filepath
    {emp_adp_cps_by_incq} % label
    {Change in Employment Rates by Wage Quartile: Paychex-Intuit vs. ADP vs. CPS \\ \vspace{3mm} May 2020} % title
    {0.495\textwidth} % width
%%%%% Panel C %%%%%
    {../results/Employment/Employment Tracker vs CPS by Income Quartile excl NY CA MA - Dec 2021} % filepath
    {emp_cps_by_incq_dec2021} % label
    {Change in Employment Rates by Wage Quartile: Paychex-Intuit vs. CPS \\ \vspace{3mm} December 2021} % title
    {0.495\textwidth} % width
%%%%% Notes %%%%%
    {This figure benchmarks the Paychex-Intuit combined employment series to the Current Population Survey (CPS) and estimates based on ADP data in Cajner et al. (2020). Panel A shows employment trends in the Paychex-Intuit combined data (solid series) and ADP data (dashed series), split by wage quartile (combined Paychex-Intuit data) or wage quintile (ADP data). The Paychex-Intuit series is expressed as a percentage change relative to January 4 to 31, 2020. The ADP series (from Cajner et al. 2020) is expressed as a percentage change relative to February 1, 2020. Panel B shows changes in employment in the Paychex-Intuit, ADP, and CPS datasets from January to May 2020, split by wage quartile. The CPS series is expressed as a percentage change relative to January 2020. The ADP series is expressed as a percentage change relative to February 1, 2020. We omit the third wage quintile of the ADP series and compare the fourth and fifth quintiles of the ADP series to the third and fourth quartiles of the Paychex-Intuit series. Panel C replicates Panel B in December 2021 for the combined Paychex-Intuit series and CPS. For Panel C, we drop CA, MA, and NY since these three states raised the minimum wage above our upper wage threshold for bottom wage quartile employment after July 2020. Data sources: Paychex, Intuit, CPS, ADP.}
    {}
```

```{=latex}
\fourpanelfig
%%%%% Full Figure Config %%%%%
    {zipmaps_emp} % label
    {Changes in Low-Wage Employment by ZIP Code} % title
%%%%% Panel A %%%%%
    {../results/Employment/Low-wage Employment vs Median Rent by Zip Binscatter} % filepath
    {employment_vs_rent_zip} % label
    {Low-Wage Employment vs. Median Rent, by ZIP \\ \vspace{3mm} July 2020} % title
%%%%% Panel B %%%%%
    {../results/Employment/map_employment_byZIP_SF} % filepath
    {zipmaps_emp_SF} % label
    {Change in Low-Wage Employment in San Francisco \\ \vspace{3mm} April 2020} % title
%%%%% Panel C %%%%%
    {../results/Employment/map_employment_byZIP_Chicago} % filepath
    {zipmaps_emp_Chicago} % label
    {Change in Low-Wage Employment in Chicago \\ \vspace{3mm} April 2020} % title
%%%%% Panel D %%%%%
    {../results/Employment/map_employment_byZIP_NYC} % filepath
    {zipmaps_emp_NYC} % label
    {Change in Low-Wage Employment in New York City \\ \vspace{3mm} April 2020} % title
%%%%% Notes %%%%%
    {Panel A shows a binned scatter plot of the relationship between low-wage employment and median rent at the ZIP code level, constructed as described in Figure \ref{fig:covid_isolation_associations}. The x-axis variable is the median rent within a ZIP code for a two-bedroom apartment in the 2014-2018 ACS. The y-axis variable is the average value of low wage employment at the ZIP code level from Earnin during the month of July 2020 (see Section \ref{subsec:Data-Employment} and Appendix \ref{sec:Data-Employment} for more detail on the construction of the Earnin employment series). Panels B, C and D replicate Appendix Figure \ref{fig:zipmaps_smallbiz} using Earnin data on changes in employment among low-wage workers, plotted by employer ZIP code. We measure the change in employment as total average weekly employment between March 27 and April 24, 2020 divided by total average weekly employment between January 4 and 31, 2020. These maps must be viewed in color to be interpretable; dark red colors represent areas with larger employment declines, while dark blue colors represent areas with smaller declines. Data sources: Earnin, ACS.}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
    {employment_covid_vs_gfc} % label
    {Geography of Employment Losses in the Great Recession vs. COVID Recession} % title
    {../results/Employment/Employment Loss by Income Quartile Great Recession vs COVID} % filepath
%%%%% Notes %%%%%
    {This figure displays the share of job losses occurring in low vs. high income counties during the Great Recession and the COVID Recession. We split counties into population-weighted quartiles by median household income in the 2006 ACS for the Great Recession (left bars) and the 2014-2018 ACS for the COVID Recession (middle and right bars). To construct the first set of four bars, we use BLS data to measure the share of the national employment losses from 2007 and 2010 occurring within counties in each quartile of median household income. The second set of bars replicates the first set of bars using the employment losses from January to April 2020. The third set of bars reports the share of total initial UI claims within each county income quartile between March 15, 2020 (the first week of COVID-related UI claims) and April 12, 2020. In this third set of bars, we only include counties within states that issue weekly reports of county-level UI claims data; these states include 53\% of the U.S. population. The increase in unemployment rates between February and April 2020 (11\%) was only two-thirds as large as the decrease in employment (16\%). The difference was due to a 5\% decline in the labor force: many people lost their jobs but were not actively searching for a new job in the midst of the pandemic (Coibion, Gorodnichenko, and Weber 2020). In the three prior recessions, the labor force continued to grow by 0.3\% to 0.8\% annually. We therefore focus on the decline in employment rates to obtain comparable statistics on job loss across recessions. Data source: BLS.}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
    {employment_changes_retail} % label
    {Changes in Employment by Wage Quartile and Consumer Spending, Retail Trade} % title
    {../results/Employment/Changes in Employment by Wage Quartile and Consumer Spending, Retail Trade - long} % filepath
%%%%% Notes %%%%%
    {This figure plots our combined Paychex-Intuit employment series for top and bottom wage quartile jobs in the Retail Trade sector (NAICS 44-45), along with our consumer spending series for this sector (see Section \ref{subsec:Data-Spending} and Appendix \ref{sec:Appx-Affinity} for details on the construction of the consumer spending series). Data sources: Paychex, Intuit, Affinity Solutions.}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
    {cps_panel_rent_gradient} % label
    {Relationship between Employment and Rent in CPS Panel} % title
    {../results/Employment/CPS Panel Benchmark - Rent Gradient by Income Quartile} % filepath
%%%%% Notes %%%%%
    {This figure plots regression estimates of the relationship between state-level changes in employment, measured separately for each wage quartile, and median two-bedroom rent in the CPS panel. We obtain the point estimates and 95\% confidence intervals from population-weighted state-level OLS regressions. We use the change in employment between each respondent's initial survey (conducted 12 months before their follow-up survey) and their follow-up survey in July 2020 to February 2021. To construct the CPS panel, we first restrict the sample to the set of individuals who were employed during their initial survey and had their follow-up survey between July 2020 and February 2021. We then classify these individuals by their wage quartile during their initial survey (July 2019 to February 2020) based on inflation-adjusted values of the Federal Poverty Line, adding uniform noise between [-\$0.50, \$0.50] to whole number wages to smooth out spikes in the wage distribution at whole numbers. See Section \ref{subsec:Data-Employment} for more details on the wage quartile thresholds and smoothing. The sample omits California, Massachusetts, and New York due to mismeasurement of bottom-quartile employment changes as a result of minimum wage increases; see Appendix \ref{subsec:Internal-Data-Processing} for more details. Data sources: CPS.}
```

```{=latex}
\twopanelfig
%%%%% Full Figure Config %%%%%
    {cps_emp} % label
    {Changes in Labor Supply due to Population Shifts} % title
%%%%% Panel A %%%%%
    {../results/Employment/Trends in Employment by Citizenship} % filepath
    {cps_by_citizenship} % label
    {Trends in Low-Wage Employment for US-Born vs. Naturalized Citizens vs. Non-Citizens} % title
%%%%% Panel B %%%%%
    {../results/Employment/Net In-Migration vs Median Rent Binscatter 2021} % filepath
    {migration_workplace_rent} % label
    {Net In-Migration vs. Rent in 2021} % title
%%%%% Notes %%%%%
    {Panel A uses CPS data to show the employment change relative to January 2020 for workers in the bottom wage quartile, comparing those who are U.S. citizens at birth, naturalized U.S. citizens, or non-U.S. citizens. Panel B presents a binned scatter plot showing the association between net in-migration shares in 2021 and median rent at the state level in the ACS 2014-2018. The binned scatter plot is constructed as described in Figure \ref{fig:covid_isolation_associations}. For each state we calculate the net in-migration share as in-migration minus out-migration divided by the state population, using Current Population Survey Annual Social and Economic Supplement (CPS ASEC). Data sources: CPS, ACS.}
```

```{=latex}
\fourpanelfig
%%%%% Full Figure Config %%%%%
    {stimulus_not_detrended} % label
    {Effects of Stimulus Payments on Spending, without Adjusting for Pre-Trends} % title
%%%%% Panel A %%%%%
    {../results/Policy/Stimulus scatterplot for April round Q1 and 4 pre line original} % filepath
    {stim1_scatter_not_detrended} % label
    {Stimulus 1 Event Study, Without Detrending} % title
%%%%% Panel B %%%%%
    {../results/Policy/Stimulus scatterplot for Jan round Q1 and 4 original} % filepath
    {stim2_scatter_not_detrended} % label
    {Stimulus 2 Event Study, Without Detrending} % title
%%%%% Panel C %%%%%
    {../results/Policy/Stimulus scatterplot for March round Q1 and 4 pre line original} % filepath
    {stim3_scatter_not_detrended} % label
    {Stimulus 3 Event Study, Without Detrending} % title
%%%%% Panel D %%%%%
    {../results/Policy/Stimulus bar chart by round and income quartile (dollars) original} % filepath
    {stim_barchart_not_detrended} % label
    {Stimulus Effect Sizes by Income Quartile, Without Detrending} % title
%%%%% Notes %%%%%
    {This figure shows event studies of spending around stimulus payments without adjusting for linear pre-trends. Panel A shows the equivalent of Figures \ref{fig:stimulus1_eventstudy_lowincome} and \ref{fig:stimulus1_eventstudy_highincome} without adjusting for linear pre-trends. Panel B is identical to Figure \ref{fig:stimulus2_eventstudy}, since both figures do not adjust for linear pre-trends. Panel C shows the equivalent of Figure \ref{fig:stimulus3_eventstudy} without adjusting for linear pre-trends. Panel D shows the equivalent of Figure \ref{fig:stimulus_effect_sizes} without adjusting for linear pre-trends. Data source: Affinity Solutions.}
```

```{=latex}
\twopanelfig
%%%%% Full Figure Config %%%%%
    {robustness} % label
    {Robustness of Stimulus Effect Size to Post-Stimulus Event Window} % title
%%%%% Panel A %%%%%
    {../results/Policy/Q1 on one plot, varying round and days (dollars)} % filepath
	{robustness_lowinc} % label
    {Bottom Income Quartile} % title
%%%%% Panel B %%%%%
    {../results/Policy/Q4 on one plot, varying round and days (dollars)} % filepath
	{robustness_highinc} % label
    {Top Income Quartile} % title
%%%%% Notes %%%%%
    {This figure displays coefficient estimates from varying post-period windows as a robustness check for the analysis displayed in Figure \ref{fig:stimulus_eventstudy}, Figure \ref{fig:stimulus_effect_sizes}, and Appendix Table \ref{tab:policy_stimulus}. Each dot corresponds to a different estimate for the “Combined Dollar” effects from Appendix Table \ref{tab:policy_stimulus}, Column 5.  Panel A plots estimates for cardholders residing in the bottom income quartile of ZIP codes, varying the post-period from 7 to 30 days, for each of the three rounds of stimulus.  Panel B repeats this analysis for cardholders residing in the top income quartile of ZIP codes. For the April 2020 and March 2021 stimulus rounds, the estimate at 25 days matches the estimate in Appendix Table \ref{tab:policy_stimulus}, Column 5; for the January 2021 stimulus round, the estimate at 16 days matches the estimate in Appendix Table \ref{tab:policy_stimulus}, Column 5. See Section \ref{subsec:Stimulus-Eval} and the notes to Figure \ref{fig:stimulus_eventstudy} and Appendix Table \ref{tab:policy_stimulus} for details on how these estimates were calculated. Data source: Affinity Solutions.}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
    {variance_week} % label
    {Daily Variance of Consumer Spending, by Week} % title
    {../results/Policy/scatter variance after first-differencing 7-day moving} % filepath
%%%%% Notes %%%%%
    {This figure depicts the within-week daily variance in the first-differenced change in indexed spending, which we construct as described in Appendix \ref{subsec:Stimulus-Policy-Data}. We then plot a 7-day trailing average of variance, omitting the points in the 6 days after holidays. The values in the Thanksgiving period range from {{thanksgiving_min}}p.p. to {{thanksgiving_max}}p.p. -- values between 800p.p. and {{thanksgiving_graph_max}}p.p. are plotted on a log scale rather than a linear scale to preserve visual clarity. Three vertical dashed lines mark the timing of deposits for each of the three rounds of stimulus payments, with light green shading to mark the pre-periods and dark green shading to mark the post-periods in the respective event studies in Figure \ref{fig:stimulus_eventstudy} and Appendix Table \ref{tab:policy_stimulus}. The red shaded dates denote holiday periods that we drop from the event study analysis (in Figure \ref{fig:stimulus2_eventstudy}) and from the permutation analysis (in Appendix Figures \ref{fig:permutations_q1} and \ref{fig:permutations_q4}). Data source: Affinity Solutions.}
```

```{=latex}
\threepanelfig
%%%%% Full Figure Config %%%%%
    {permutations_q1} % label
    {Permutation Test for Differences in Stimulus Effects, Bottom Income Quartile} % title
%%%%% Panel A %%%%%
    {../results/Policy/histogram permutation triple-diff Stim1v2 Q1} % filepath
    {perm_stim1v2_lowinc} % label
    {1st Stimulus Minus 2nd Stimulus, Bottom Income Quartile} % title
%%%%% Panel B %%%%%
    {../results/Policy/histogram permutation triple-diff Stim1v3 Q1} % filepath
    {perm_stim1v3_lowinc} % label
    {1st Stimulus Minus 3rd Stimulus, Bottom Income Quartile} % title
%%%%% Panel C %%%%%
    {../results/Policy/histogram permutation triple-diff Stim2v3 Q1} % filepath
    {perm_stim2v3_lowinc} % label
    {2nd Stimulus Minus 3rd Stimulus, Bottom Income Quartile} % title
%%%%% Notes %%%%%
    {These figures present a permutation test of the null hypothesis that there is no difference in spending impacts across rounds of stimulus payments for households in the bottom income quartile of ZIP codes. We consider all placebo dates for stimulus payments from August 1, 2020 to May 1, 2021, dropping dates that fall within 25 days of the beginning or end of an actual pre- or post-stimulus window or within a high-variance holiday period (see Appendix Figure \ref{fig:variance_week} for more details). We then calculate the “combined dollar” estimate for each placebo date (as in Appendix Table \ref{tab:policy_stimulus}, Column 5) for each income quartile and stimulus round, following the approach described in Appendix \ref{subsec:Stimulus-Policy-Rescaling}. We include 25 days pre-event and then either 25 days post-event (1st and 3rd stimulus) or 16 days post-event (2nd stimulus). We drop days in the pre- or post-period that fall within a holiday period, and we omit the estimate entirely if a holiday falls in the post-period. We adjust for linear pre-trends in both the treatment and control series whenever there are more than 20 days in the pre-period. This leads to 89 placebo estimates for the first stimulus and third stimulus, and 59 placebo estimates for the second stimulus. We take the difference between each possible pair of placebo estimates (1st minus the 2nd stimulus; 1st minus the 3rd stimulus; and 2nd minus the 3rd stimulus) and plot the distribution of the calculated difference in “Combined Dollar” effects. Panel A plots the distribution of placebo differences between the 1st minus the 2nd stimulus. Panel B repeats this for the 1st minus the 3rd stimulus, and Panel C repeats this for the 2nd minus the 3rd stimulus. On each panel, we mark the actual difference in estimates (taken from the appropriate difference between estimates in Appendix Table \ref{tab:policy_stimulus}, Column 5) with a dashed vertical line. We also report the standard deviation of the placebo draws, the 95\% confidence interval (the smallest interval that covers 95\% of placebo differences), and the two-sided p-value (twice the minimum of the fraction of placebo differences to the right of the actual estimate and the fraction to the left of the actual estimate). Data source: Affinity Solutions.}
```

```{=latex}
\threepanelfig
%%%%% Full Figure Config %%%%%
    {permutations_q4} % label
    {Permutation Test for Differences in Stimulus Effects, Top Income Quartile} % title
%%%%% Panel A %%%%%
    {../results/Policy/histogram permutation triple-diff Stim1v2 Q4} % filepath
    {perm_stim1v2_highinc} % label
    {1st Stimulus Minus 2nd Stimulus, Top Income Quartile} % title
%%%%% Panel B %%%%%
    {../results/Policy/histogram permutation triple-diff Stim1v3 Q4} % filepath
    {perm_stim1v3_highinc} % label
    {1st Stimulus Minus 3rd Stimulus, Top Income Quartile} % title
%%%%% Panel C %%%%%
    {../results/Policy/histogram permutation triple-diff Stim2v3 Q4} % filepath
    {perm_stim2v3_highinc} % label
    {2nd Stimulus Minus 3rd Stimulus, Top Income Quartile} % title
%%%%% Notes %%%%%
    {These figures present a permutation test of the null hypothesis that there is no difference in spending impacts across rounds of stimulus payments for households in the top income quartile of ZIP codes. Each panel is constructed analogously to the corresponding panel in Appendix Figure \ref{fig:permutations_q1}. Data source: Affinity Solutions.}
```

```{=latex}
\ThreePanelFigCustomWidth
%%%%% Full Figure Config %%%%%
    {reopenings} % label
    {Effects of Reopenings on Economic Activity} % title
%%%%% Panel A %%%%%
    {../results/State Re-Openings/Colorado vs New Mexico Re-Opening Event Study} % filepath
    {colorado_newmexico} % label
    {Case Study: Colorado vs New Mexico} % title
    {0.5\textwidth} % width
%%%%% Panel B %%%%%
    {../results/State Re-Openings/Re-Opened vs Control States - All Outcomes} % filepath
    {reopenings_eventstudies} % label
    {Stacked Event Study: Re-Opened States vs. Control States} % title
    {0.8\textwidth} % width
%%%%% Panel C %%%%%
    {../results/State Re-Openings/Variance Explained by Reopenings} % filepath
    {reopenings_variance} % label
    {Variance Explained by Reopenings \\ \vspace{3mm} May 2020} % title
    {0.5\textwidth} % width
%%%%% Notes %%%%%
    {Panel A plots the change in seasonally-adjusted consumer spending relative to January 6 to February 2, 2020 for New Mexico and Colorado. Colorado partially reopened non-essential businesses on May 1, 2020, while New Mexico did not do so until May 16, 2020. Panel B1 plots an event study of the same outcome variable for five “treated” states (SC, AK, GA, MN and MS) that partially reopened non-essential businesses between April 20 and 27, 2020. For each reopening, the treated states are matched to multiple control states (listed in Appendix Table \ref{tab:reopenings_states}) that did not reopen within the subsequent 3 weeks but had similar trends of the outcome variable in the preceding 3 weeks. We then stack the resulting event studies by time relative to the reopenings. Panels B2 and B3 replicate Panel B1 with, respectively, the change in employment and the seasonally-adjusted change in small businesses open. In Panels B1 to B3, we report the coefficient from a difference-in-differences regression comparing treated vs. untreated states in the two weeks after vs. the two weeks before the reopening (also reported in Appendix Table \ref{tab:reopening_effects}). Panel C reports the share of the variance in outcomes explained by reopenings as of May 18, 2020. To estimate these variance shares, we first calculate the variance of each outcome across states on May 18, 2020. Then, we add the difference-in-differences estimate for the effect of reopening on a given outcome to all states not open on May 18 (adding only half of the effect if the state opened between May 11 and 18, 2020). We then recalculate the variance in this counterfactual in which all states had reopened. The share of variance explained by reopenings for each outcome is 1 $-$ (counterfactual variance/actual variance). Data sources: Affinity Solutions, Paychex, Intuit, Womply.}
    {} %footnote font size
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
    {closures} % label
    {Effects of State-Ordered Business Closures on Consumer Spending} % title
    {../results/Spending/Consumer Spending around State-Ordered Business Closures} % filepath
%%%%% Notes %%%%%
    {This figure displays trends in seasonally-adjusted consumer spending in the Affinity Solutions data, pooling states by the date on which a state-wide order closed non-essential businesses and weighting by state population. States are aggregated into three groups: “Early” (state-wide closure order issued between March 17 and 24, 2020), “Late” (state-wide closure order issued between March 30 and April 7, 2020), and “Non-Closers” (no state-wide closure order issued by April 7, 2020). Dashed lines denote the first date on which state-wide orders closing non-essential businesses were issued by “Early Closers” (March 17) and “Late Closers” (March 30). The blue and grey shaded areas denote the range of closure dates for “Early Closers” and “Late Closers” respectively. Data source: Affinity Solutions.}
```

```{=latex}
\twopanelfig
%%%%% Full Figure Config %%%%%
    {ppp_employment} % label
    {Effects of the Paycheck Protection Program on Employment} % title
%%%%% Panel A %%%%%
    {../results/Employment/Change in Employment by PPP Eligibility} % filepath
	{emp_ppp} % label
    {Change in Employment by PPP Eligibility, All Industries Excluding Food Services} % title
%%%%% Panel B %%%%%
	{../results/Employment/Change in Employment by Firm Size} % filepath
	{emp_firmsize} % label
    {Change in Employment by Firm Size, All Industries Excluding Food Services \\ \vspace{3mm} June 2020} % title
%%%%% Notes %%%%%
    {This figure analyzes the effects of the Paycheck Protection Program on employment using the threshold in eligibility at 500 employees. We pool all industries except Accommodation and Food Services (NAICS 72), which was subject to different eligibility rules (discussed in Appendix \ref{subsec:Paycheck-Protection-Program}). Panel A compares employment trends measured in Paychex and Earnin data among firms with 100-499 employees (generally eligible for PPP loans) to firms with 500-799 employees (generally ineligible for PPP loans). To construct these employment trends, we begin by calculating weekly employment changes relative to January 4 to 31, 2020 disaggregated by county, industry (2-digit NAICS), wage quartile and firm size bin. We reweight these cells so that employment shares by industry within each eligibility group match the overall employment shares by industry over January 4 to 31, 2020. We plot the “control” series (firms with 500-799 employees) directly as the mean weekly value of the reweighted employment series. We plot the “treated” series (firms with 100-499 employees) as the sum of the control series and the coefficients from an event study specification (controlling for county x wage quartile x week fixed effects and leaving out January 3, 2020 as the reference dummy). We use reweighted employment over January 4 to 31, 2020 as regression weights. The regression estimate in Column 1 of Appendix Table \ref{tab:ppp_effects}, which uses an estimation window of March 11 to August 15, 2020, is also reported in the figure. Panel B presents a binned scatter plot of changes in reweighted employment from January to June 2020 vs. firm size. Data sources: Paychex, Earnin.}
```
